A Learning Algorithm and System Approach to Address Exceptional Events in Domestic Consumption Management

The integration of the Smart Grid concept into the electric grid brings to the need for an active participation of small and medium players. This active participation can be achieved using decentralized decisions, in which the end consumer can manage loads regarding the Smart Grid needs. The management of loads must handle the users’ preferences, wills and needs. However, the users’ preferences, wills and needs can suffer changes when faced with exceptional events. This paper proposes the integration of exceptional events into the SCADA House Intelligent Management (SHIM) system developed by the authors, to handle machine learning issues in the domestic consumption context. An illustrative application and learning case study is provided in this paper.

Intelligent decision making in electricity markets: simulated annealing Q-Learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

Cost dependent strategy for electricity markets bidding based on adaptive reinforcement learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM provides several dynamic strategies for agents’ behavior. This paper presents a method that aims to provide market players with strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible bids. These bids are defined accordingly to the cost function that each producer presents.

Reinforcement Learning Based on the Bayesian Theorem for Electricity Markets Decision Support

This paper presents the applicability of a reinforcement learning algorithm based on the application of the Bayesian theorem of probability. The proposed reinforcement learning algorithm is an advantageous and indispensable tool for ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to electricity market negotiating players. ALBidS uses a set of different strategies for providing decision support to market players. These strategies are used accordingly to their probability of success for each different context. The approach proposed in this paper uses a Bayesian network for deciding the most probably successful action at each time, depending on past events. The performance of the proposed methodology is tested using electricity market simulations in MASCEM (Multi-Agent Simulator of Competitive Electricity Markets). MASCEM provides the means for simulating a real electricity market environment, based on real data from real electricity market operators.

Híper-heurísticas com aprendizagem

A otimização nos sistemas de suporte à decisão atuais assume um carácter fortemente interdisciplinar relacionando-se com a necessidade de integração de diferentes técnicas e paradigmas na resolução de problemas reais complexos, sendo que a computação de soluções ótimas em muitos destes problemas é intratável. Os métodos de pesquisa heurística são conhecidos por permitir obter bons resultados num intervalo temporal aceitável. Muitas vezes, necessitam que a parametrização seja ajustada de forma a permitir obter bons resultados. Neste sentido, as estratégias de aprendizagem podem incrementar o desempenho de um sistema, dotando-o com a capacidade de aprendizagem, por exemplo, qual a técnica de otimização mais adequada para a resolução de uma classe particular de problemas, ou qual a parametrização mais adequada de um dado algoritmo num determinado cenário. Alguns dos métodos de otimização mais usados para a resolução de problemas do mundo real resultaram da adaptação de ideias de várias áreas de investigação, principalmente com inspiração na natureza - Meta-heurísticas. O processo de seleção de uma Meta-heurística para a resolução de um dado problema é em si um problema de otimização. As Híper-heurísticas surgem neste contexto como metodologias eficientes para selecionar ou gerar heurísticas (ou Meta-heurísticas) na resolução de problemas de otimização NP-difícil. Nesta dissertação pretende-se dar uma contribuição para o problema de seleção de Metaheurísticas respetiva parametrização. Neste sentido é descrita a especificação de uma Híperheurística para a seleção de técnicas baseadas na natureza, na resolução do problema de escalonamento de tarefas em sistemas de fabrico, com base em experiência anterior. O módulo de Híper-heurística desenvolvido utiliza um algoritmo de aprendizagem por reforço (QLearning), que permite dotar o sistema da capacidade de seleção automática da Metaheurística a usar no processo de otimização, assim como a respetiva parametrização. Finalmente, procede-se à realização de testes computacionais para avaliar a influência da Híper- Heurística no desempenho do sistema de escalonamento AutoDynAgents. Como conclusão genérica, é possível afirmar que, dos resultados obtidos é possível concluir existir vantagem significativa no desempenho do sistema quando introduzida a Híper-heurística baseada em QLearning.

Contextual intelligent load management with ANN adaptive learning module

With the current increase of energy resources prices and environmental concerns intelligent load management systems are gaining more and more importance. This paper concerns a SCADA House Intelligent Management (SHIM) system that includes an optimization module using deterministic and genetic algorithm approaches. SHIM undertakes contextual load management based on the characterization of each situation. SHIM considers available generation resources, load demand, supplier/market electricity price, and consumers’ constraints and preferences. The paper focus on the recently developed learning module which is based on artificial neural networks (ANN). The learning module allows the adjustment of users’ profiles along SHIM lifetime. A case study considering a system with fourteen discrete and four variable loads managed by a SHIM system during five consecutive similar weekends is presented.

Machine Learning Algorithms at Myocardial Perfusion Imaging - a Preliminary Study

Introduction: A major focus of data mining process - especially machine learning researches - is to automatically learn to recognize complex patterns and help to take the adequate decisions strictly based on the acquired data. Since imaging techniques like MPI – Myocardial Perfusion Imaging on Nuclear Cardiology, can implicate a huge part of the daily workflow and generate gigabytes of data, there could be advantages on Computerized Analysis of data over Human Analysis: shorter time, homogeneity and consistency, automatic recording of analysis results, relatively inexpensive, etc.Objectives: The aim of this study relates with the evaluation of the efficacy of this methodology on the evaluation of MPI Stress studies and the process of decision taking concerning the continuation – or not – of the evaluation of each patient. It has been pursued has an objective to automatically classify a patient test in one of three groups: “Positive”, “Negative” and “Indeterminate”. “Positive” would directly follow to the Rest test part of the exam, the “Negative” would be directly exempted from continuation and only the “Indeterminate” group would deserve the clinician analysis, so allowing economy of clinician’s effort, increasing workflow fluidity at the technologist’s level and probably sparing time to patients. Methods: WEKA v3.6.2 open source software was used to make a comparative analysis of three WEKA algorithms (“OneR”, “J48” and “Naïve Bayes”) - on a retrospective study using the comparison with correspondent clinical results as reference, signed by nuclear cardiologist experts - on “SPECT Heart Dataset”, available on University of California – Irvine, at the Machine Learning Repository. For evaluation purposes, criteria as “Precision”, “Incorrectly Classified Instances” and “Receiver Operating Characteristics (ROC) Areas” were considered. Results: The interpretation of the data suggests that the Naïve Bayes algorithm has the best performance among the three previously selected algorithms. Conclusions: It is believed - and apparently supported by the findings - that machine learning algorithms could significantly assist, at an intermediary level, on the analysis of scintigraphic data obtained on MPI, namely after Stress acquisition, so eventually increasing efficiency of the entire system and potentially easing both roles of Technologists and Nuclear Cardiologists. In the actual continuation of this study, it is planned to use more patient information and significantly increase the population under study, in order to allow improving system accuracy.

Enhancing reflexivity and technology in translation learning: case study on the use of ePortfolio as a training environment

In this paper we describe a casestudy of an experiment on how reflexivity and technology can enhance learning, by using ePorfolios as a training environment to develop translation skills. Translation is today a multiskilled job and translators need to assure their clients a good performance and quality, both in language and in technology domains. In order to accomplish it, for the translator all the tasks and processes he develops appear as crucial, being pretranslation and posttranslation processes equally important as the translation itself, namely as far as autonomy, reflexive and critical skills are concerned. Finally, the need and relevance for collaborative tasks and networks amongst virtual translation communities, led us to the decision of implementing ePortfolios as a tool to develop the requested skills and extend the use of Internet in translation, namely in terminology management phases, for the completion of each task, by helping students in the management of the projects deadlines, improving their knowledge on the construction and management of translation resources and deepening their awareness about the concepts related to the development and usability of ePorfolios.

Learning management systems usage on higher education institutions

Learning Management Systems (LMS) are used all over Higher Education Institutions (HEI) and the need to know and understand its adoption and usage arises. However, there is a lack of information about how LMSs are being used, which are the most adopted, whether there is a country adoption standard and which countries use more LMSs. A research team is developing a project that tries to fill this lack of information and provide the needed answers. With this purpose, on a first phase, it a survey was taken place. The results of this survey are presented in this paper. Another purpose of this paper is to disseminate the ongoing project.

Accounting education in an action-based learning environment: lessons learned

In the context of the Bologna Declaration a change is taking place in the teaching/learning paradigm. From teaching-centered education, which emphasizes the acquisition and transmission of knowledge, we now speak of learning-centered education, which is more demanding for students. This paradigm promotes a continuum of lifelong learning, where the individual needs to be able to handle knowledge, to select what is appropriate for a particular context, to learn permanently and to understand how to learn in new and rapidly changing situations. One attempt to face these challenges has been the experience of ISCAP regarding the teaching/learning of accounting in the course Managerial Simulation. This paper describes the process of teaching, learning and assessment in an action-based learning environment. After a brief general framework that focuses on education objectives, we report the strengths and limitations of this teaching/learning tool. We conclude with some lessons from the implementation of the project.

Formação combinada: reutilizando learning objects

O uso crescente da Internet (World Wide Web), e das suas potencialidades tecnológicas têm contribuído para uma proliferação de ambientes de ensino/aprendizagem, baseados em Tecnologia. A comunidade científica reúne consenso quanto às vantagens da reutilização de conteúdos de aprendizagem e à adopção de standards com vista à interoperabilidade entre conteúdos/objectos partilháveis e plataformas. Este artigo tem como objectivo reflectir sobre o desenvolvimento de uma metodologia de ensino combinada de aprendizagem com recurso a Learning Objects, no âmbito do trabalho de doutoramento.

Thinking over b‐learning strategy: the MIPO model approach

This article describes the main research results in a new methodology, in which the stages and strategies of the technology integration process are identified and described. A set of principles and recommendations are therefore presented. The MIPO model described in this paper is a result of the effort made regarding the understanding of the main success features of good practices, in the web environment, integrated in the information systems/information technology context. The initial model has been created, based on experiences and literature review. After that, it was tested in the information and technology system units at higher school and also adapted as a result of four cycles of an actionresearch work combined with a case study research. The information, concepts and procedures presented here give support to teachers and instructors, instructional designers and planning teams – anyone who wants to develop effective b‐learning instructions.

A Game of learning: a b-activity

Th The purpose of this article is to share the implementation of workgroup activities: a game of learning supported by web technology; Effective educational strategies that encourage a dynamic combination of being flexible, individualized and personalized must be the aim of every school; The blended-learning plays an important role; In this article we describe an online collaborative game which uses an inside and outside collaboration in order to promote the motivation and effective learning; Pedagogical strategies, that use technologies appropriately, in higher education, can promote active learning, centered on students and thus valuing their personal experiences and participation;

Challenges for e-learning and adult students in higher education

This article examines Lifelong Learning, from the perspective of the adult learner in higher education, by presenting some of the results of a project, funded by the European Commission's Socrates Programme, LIHE, Learning in Higher Education. It is structured as follows: first, the background of the project is described, then the experiences of the adult student, concerning their induction and tuition, are presented. Some future trends concerning adults in higher education and lifelong learning are outlined and conclusions drawn.

Building a knowledge and learning society in Portugal: adult students in technological schools and higher education institutions

Lifelong learning (LLL) has received increasing attention in recent years. It implies that learning should take place at all stages of the “life cycle and it should be life-wide, that is embedded in all life contexts from the school to the work place, the home and the community” (Green, 2002, p.613). The ‘learning society’, is the vision of a society where there are recognized opportunities for learning for every person, wherever they are and however old they happen to be. Globalization and the rise of new information technologies are some of the driving forces that cause depreciation of specialised competences. This happens very quickly in terms of economic value; consequently, workers of all skills levels, during their working life, must have the opportunity to update “their technical skills and enhance general skills to keep pace with continuous technological change and new job requirements” (Fahr, 2005, p. 75). It is in this context that LLL tops the policy agenda of international bodies, national governments and non-governmental organizations, in the field of education and training, to justify the need for LLL opportunities for the population as they face contemporary employability challenges. It is in this context that the requirement and interest to analyse the behaviour patterns of adult learners has developed over the last few years